Technical Lead – Data Science, AI

Euromonitor International

. Set technical direction for platform components across retrieval, ranking, evaluation and agentic services .

Posted 10/13/2026full-timeBengaluru • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in software engineering with a focus on Data Science and AI, particularly in applied Machine Learning, backend service architecture, and the development of reusable services. Proven ability to lead design decisions, mentor engineers, and ensure production readiness in cloud-native environments.

Highest-signal resume keywords

Applied Machine LearningBackend Service ArchitectureLLM Orchestration FrameworksProduction Operations on AzureDesign Review Leadership

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonC#JavaGoMachine LearningNLPStatistical AnalysisModel DevelopmentEvaluation FrameworksHybrid Search

Soft Skills

MentoringLeadershipCollaborationTechnical Direction

Tools & Technologies

KubernetesAzure Machine LearningMLflowSageMakerVertex AIElasticsearchLangGraphLangChainRagasDeepEval

Industry Keywords

Market ResearchPublishingInformation ServicesAccuracy-Sensitive Domain

Tech Stack

Tools & technologies

AWSAzureCloudElasticSearchGoogle Cloud PlatformJavaKubernetesOpen SourcePythonPyTorchScikit-LearnTensorflowGo.NET

About the role

Key responsibilities & impact

Set technical direction for platform components across retrieval, ranking, evaluation and agentic services
Own design decisions that keep platform components coherent
Lead design and code reviews
Establish engineering standards
Mentor senior and associate engineers across data science and backend disciplines
Make build, buy or adopt decisions on frameworks and tooling, documenting trade-offs on cost, lock-in and maintainability
Guide reusable ranking, personalisation and recommendation components from experimentation to shared services
Own technical quality of the evaluation harness, including golden datasets, LLM-as-judge calibration and regression gates
Steer retrieval strategy across structured and unstructured data, including chunking, indexing, hybrid search, reranking and embedding lifecycle
Architect and build versioned, documented backend services and APIs for the AI platform
Build the agent framework and skills and tool registry
Ensure production readiness through observability, cost and token telemetry, quality gates and Responsible AI controls
Partner with Engineering, Architecture and Data Platform teams
Align the platform with Euromonitor's cloud-native architecture
Onboard consuming teams through reference implementations and paved-road patterns

Requirements

What you’ll need

10+ years in software engineering with substantial applied Data Science and AI experience
Hands-on technical lead experience accountable for others' designs and your own
Applied Machine Learning depth in ranking, recommendation or NLP, with statistical grounding for designing and interpreting evaluations
Modern Generative AI engineering experience
Backend service architecture experience
Track record designing reusable services, libraries or frameworks adopted by multiple teams
Experience with RAG and retrieval systems
Experience with LLM orchestration frameworks such as LangGraph, LangChain, LlamaIndex or Semantic Kernel; LangGraph and LangChain preferred
Experience with vector or hybrid search such as Elasticsearch, OpenSearch, Azure AI Search or pgvector; Elasticsearch preferred
Production operations on Azure, AWS or GCP with Kubernetes; Azure and AKS preferred
Strong Python plus at least one further backend language such as C#, Java or Go; C# and .NET preferred
Experience leading design reviews and mentoring engineers
Experience with MCP or tool calling protocols, agent runtimes, or LLM gateway patterns
Experience with LLM-as-judge calibration, golden dataset methodology or online experimentation
Experience with evaluation frameworks such as Ragas, DeepEval or promptfoo
Model development experience with PyTorch, TensorFlow or scikit-learn, including training or fine-tuning beyond API-level foundation model usage
Exposure to AutoGen, CrewAI, OpenAI Agents SDK, MLflow, Azure Machine Learning, SageMaker or Vertex AI
Contribution to internal platforms, open source libraries or SDKs with real external consumers
Background in market research, publishing, information services or another accuracy-sensitive domain

Benefits

Comp & perks

Flexible hours
Regular social events, including after-work meetups, summer and Christmas parties
Two volunteering days annually
Competitive salaries
Private health insurance
Generous holiday allowances
Extensive training and development
Opportunities for promotion from within and across departments
Equal employment opportunity